hmm_example_finale.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from file_hopping import Txt_hop\n",
    "%matplotlib inline\n",
    "from graph_hop import Graph_hop\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# directory name\n",
    "dir_name = \"P5bp_3bs_hop/14cp\"\n",
    "# molecule name is a number in these experimets\n",
    "number = \"14\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = Txt_hop(dir_name, number, different_name=\"hopcp\", first_time_molecule=False)\n",
    "dataFrame = file.bricolage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X_force</th>\n",
       "      <th>Z_force</th>\n",
       "      <th>lambda</th>\n",
       "      <th>Y_force</th>\n",
       "      <th>time(sec)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-0.5730</td>\n",
       "      <td>-0.0932</td>\n",
       "      <td>-192.22785</td>\n",
       "      <td>4.6908</td>\n",
       "      <td>20552.01758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-0.0076</td>\n",
       "      <td>-0.0236</td>\n",
       "      <td>-191.47140</td>\n",
       "      <td>4.5292</td>\n",
       "      <td>20552.01758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.1529</td>\n",
       "      <td>-0.0274</td>\n",
       "      <td>-192.32165</td>\n",
       "      <td>4.5898</td>\n",
       "      <td>20552.01953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-0.0495</td>\n",
       "      <td>-0.0323</td>\n",
       "      <td>-192.01725</td>\n",
       "      <td>4.4383</td>\n",
       "      <td>20552.01953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-0.1961</td>\n",
       "      <td>0.1707</td>\n",
       "      <td>-192.18140</td>\n",
       "      <td>4.2211</td>\n",
       "      <td>20552.02148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51896</td>\n",
       "      <td>-0.4787</td>\n",
       "      <td>-0.1396</td>\n",
       "      <td>-202.11860</td>\n",
       "      <td>4.5847</td>\n",
       "      <td>20685.33984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51897</td>\n",
       "      <td>-0.1856</td>\n",
       "      <td>-0.0700</td>\n",
       "      <td>-201.67610</td>\n",
       "      <td>4.5241</td>\n",
       "      <td>20685.33984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51898</td>\n",
       "      <td>0.0238</td>\n",
       "      <td>-0.0700</td>\n",
       "      <td>-201.95490</td>\n",
       "      <td>4.7059</td>\n",
       "      <td>20685.34180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51899</td>\n",
       "      <td>-0.0285</td>\n",
       "      <td>-0.1048</td>\n",
       "      <td>-201.14295</td>\n",
       "      <td>4.7413</td>\n",
       "      <td>20685.34180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51900</td>\n",
       "      <td>-0.6515</td>\n",
       "      <td>-0.0787</td>\n",
       "      <td>-201.94345</td>\n",
       "      <td>4.6706</td>\n",
       "      <td>20685.34370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>51901 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       X_force  Z_force     lambda  Y_force    time(sec)\n",
       "0      -0.5730  -0.0932 -192.22785   4.6908  20552.01758\n",
       "1      -0.0076  -0.0236 -191.47140   4.5292  20552.01758\n",
       "2       0.1529  -0.0274 -192.32165   4.5898  20552.01953\n",
       "3      -0.0495  -0.0323 -192.01725   4.4383  20552.01953\n",
       "4      -0.1961   0.1707 -192.18140   4.2211  20552.02148\n",
       "...        ...      ...        ...      ...          ...\n",
       "51896  -0.4787  -0.1396 -202.11860   4.5847  20685.33984\n",
       "51897  -0.1856  -0.0700 -201.67610   4.5241  20685.33984\n",
       "51898   0.0238  -0.0700 -201.95490   4.7059  20685.34180\n",
       "51899  -0.0285  -0.1048 -201.14295   4.7413  20685.34180\n",
       "51900  -0.6515  -0.0787 -201.94345   4.6706  20685.34370\n",
       "\n",
       "[51901 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean(mu1, mu2):\n",
    "    return (mu1+mu2)/2\n",
    "\n",
    "def err_prop_mean(sigma1, sigma2):\n",
    "    return np.sqrt(sigma1**2+sigma2**2)/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f media vale = 4.749707252635607, con deviazione standard = 0.3054215094786547\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grafici = Graph_hop(dir_name, number, 1)\n",
    "data_frame = grafici.do_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data_frame['Y_force'][:600]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c_1 = 1.7683136033524405, mu_1 = 4.926071035488431, sigma_1 = 0.1491255854167658\n",
      "c_2 = 0.730071736399064, mu_2 = 4.385061053917408, sigma_2 = 0.18098993755108378\n",
      "sigma_c_1 = 0.03979856366512559, sigma_mu_1 = 0.005099541746847006, sigma_sigma_1 = 0.0053926136341325885\n",
      "sigma_c_2 = 0.035368766756451855, sigma_mu_2 = 0.013293207992491491, sigma_sigma_2 = 0.013356362518200663\n",
      "w_U = 0.33121492624375987, sigma_w_U = 0.04025291182323126\n",
      "w_N = 0.6609998845823418, sigma_w_N = 0.009562063474526875\n"
     ]
    }
   ],
   "source": [
    "prova = [2., 4.9, 0.1, 0.5, 4.4, 0.1]\n",
    "fitting_par, fitting_err, prob, err_prob = grafici.fit(guess=prova)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hmm import HMM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = [[0.92, 0.08], [0.03, 0.97]] # taken by an article\n",
    "PI = [0.5, 0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hidden = HMM(2, A, PI, data_frame['Y_force'][:600], fitting_par)\n",
    "# alpha, beta = hidden.evaluation_problem()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# alpha, beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X = hidden.uncovering_problem()\n",
    "# X\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grafici.subplots(fitting_par, n_points_fig=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A_2, PI_2 = hidden.learning_problem()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A_2, PI_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# alpha, beta, X_1, A_1, PI_1 = hidden.iteration(MAX_ITERATION=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# alpha, beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X, A, PI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A_1, PI_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.000000000000001"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.9954570968351122 + 0.004542903164888855"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999999999999984"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.005647760425847149 + 0.9943522395741513"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grafici.subplots(fitting_par, n_points_fig=600, X=X_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hidden = HMM(2, A, PI, data_frame['Y_force'].iloc[600:1200].reset_index()['Y_force'], fitting_par)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# alpha, beta, X_2, A_2, PI_2 = hidden.iteration(MAX_ITERATION=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A_2, PI_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grafici.subplots(fitting_par, n_points_fig=1200, X=X_1+X_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hidden = HMM(2, A, PI, data_frame['Y_force'].iloc[1200:1800].reset_index()['Y_force'], fitting_par)\n",
    "# alpha, beta, X_3, A_3, PI_3 = hidden.iteration(MAX_ITERATION=200)\n",
    "# A_3, PI_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grafici.subplots(fitting_par, n_points_fig=1800, X=X_1+X_2+X_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hidden = HMM(2, A, PI, data_frame['Y_force'].iloc[1800:2400].reset_index()['Y_force'], fitting_par)\n",
    "# alpha, beta, X_4, A_4, PI_4 = hidden.iteration(MAX_ITERATION=200)\n",
    "# A_4, PI_4\n",
    "# grafici.subplots(fitting_par, n_points_fig=2400, X=X_1+X_2+X_3+X_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hidden = HMM(2, A, PI, data_frame['Y_force'].iloc[2400:3000].reset_index()['Y_force'], fitting_par)\n",
    "# alpha, beta, X_5, A_5, PI_5 = hidden.iteration(MAX_ITERATION=200)\n",
    "# A_5, PI_5\n",
    "# grafici.subplots(fitting_par, n_points_fig=3000, X=X_1+X_2+X_3+X_4+X_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hidden = HMM(2, A, PI, data_frame['Y_force'].iloc[3000:3600].reset_index()['Y_force'], fitting_par)\n",
    "# alpha, beta, X_6, A_6, PI_6 = hidden.iteration(MAX_ITERATION=200)\n",
    "# A_6, PI_6\n",
    "# grafici.subplots(fitting_par, n_points_fig=3600, X=X_1+X_2+X_3+X_4+X_5+X_6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hidden = HMM(2, A, PI, data_frame['Y_force'].iloc[3600:4200].reset_index()['Y_force'], fitting_par)\n",
    "# alpha, beta, X_7, A_7, PI_7 = hidden.iteration(MAX_ITERATION=200)\n",
    "# A_7, PI_7\n",
    "# grafici.subplots(fitting_par, n_points_fig=4200, X=X_1+X_2+X_3+X_4+X_5+X_6+X_7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hidden = HMM(2, A, PI, data_frame['Y_force'].iloc[4200:4800].reset_index()['Y_force'], fitting_par)\n",
    "# alpha, beta, X_8, A_8, PI_8 = hidden.iteration(MAX_ITERATION=200)\n",
    "# A_8, PI_8\n",
    "# grafici.subplots(fitting_par, n_points_fig=4800, X=X_1+X_2+X_3+X_4+X_5+X_6+X_7+X_8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hidden = HMM(2, A, PI, data_frame['Y_force'].iloc[4800:5400].reset_index()['Y_force'], fitting_par)\n",
    "# alpha, beta, X_9, A_9, PI_9 = hidden.iteration(MAX_ITERATION=200)\n",
    "# A_9, PI_9\n",
    "# grafici.subplots(fitting_par, n_points_fig=5400, X=X_1+X_2+X_3+X_4+X_5+X_6+X_7+X_8+X_9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hidden = HMM(2, A, PI, data_frame['Y_force'].iloc[5400:6000].reset_index()['Y_force'], fitting_par)\n",
    "# alpha, beta, X_10, A_10, PI_10 = hidden.iteration(MAX_ITERATION=200)\n",
    "# A_10, PI_10\n",
    "# grafici.subplots(fitting_par, n_points_fig=6000, X=X_1+X_2+X_3+X_4+X_5+X_6+X_7+X_8+X_9+X_10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hidden = HMM(2, A, PI, data_frame['Y_force'].iloc[6000:6600].reset_index()['Y_force'], fitting_par)\n",
    "# alpha, beta, X_11, A_11, PI_11 = hidden.iteration(MAX_ITERATION=200)\n",
    "# A_11, PI_11\n",
    "# grafici.subplots(fitting_par, n_points_fig=6600, X=X_1+X_2+X_3+X_4+X_5+X_6+X_7+X_8+X_9+X_10+X_11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grafici.subplots(fitting_par, n_points_fig=6600, X=X_1+X_2+X_3+X_4+X_5+X_6+X_7+X_8+X_9+X_10+X_11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOADING 10.0%\n",
      "LOADING 20.0%\n",
      "LOADING 30.0%\n",
      "LOADING 40.0%\n",
      "LOADING 50.0%\n",
      "LOADING 60.0%\n",
      "LOADING 70.0%\n",
      "LOADING 80.0%\n",
      "LOADING 90.0%\n",
      "LOADING 100.0%\n"
     ]
    }
   ],
   "source": [
    "X_fin = []\n",
    "for i in range(0,6000,600):\n",
    "    hidden = HMM(2, A, PI, data_frame['Y_force'].iloc[i:i+600].reset_index()['Y_force'], fitting_par)\n",
    "    alpha, beta, X_1, A_1, PI_1 = hidden.iteration(MAX_ITERATION=200, show=False)\n",
    "    X_fin.extend(X_1)\n",
    "    print(f\"LOADING {i/60+10}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grafici.subplots(fitting_par, n_points_fig=6000, X=X_fin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import imageio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "filenames = []\n",
    "states = []\n",
    "for j in X_fin:\n",
    "    if j == 0:\n",
    "        states.append(fitting_par[1])\n",
    "    else:\n",
    "        states.append(fitting_par[4])\n",
    "for i in range(0, len(states), 100):\n",
    "    # plot the line chart\n",
    "    plt.plot(data_frame['time(sec)'][:6000], data_frame['Y_force'][:6000], label='$f_y$')\n",
    "    plt.plot(data_frame['time(sec)'][:i], states[:i], linewidth=3, color = 'c', label='HMM')\n",
    "    plt.xlabel('$t\\:[s]$')\n",
    "    plt.ylabel('$f_y\\:[pN]$')\n",
    "    plt.legend()\n",
    "    # create file name and append it to a list\n",
    "    filename = f'{i}.png'\n",
    "    filenames.append(filename)\n",
    "    \n",
    "    # save frame\n",
    "    plt.savefig(filename)\n",
    "    plt.close()\n",
    "# build gif\n",
    "with imageio.get_writer('hmm.gif', mode='I') as writer:\n",
    "    for filename in filenames:\n",
    "        image = imageio.imread(filename)\n",
    "        writer.append_data(image)\n",
    "        \n",
    "# Remove files\n",
    "for filename in set(filenames):\n",
    "    os.remove(filename)"
   ]
  }
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